Latent Social Information in Group Interactions with a Shared Workspace

Shared artifacts, such as drawings and schemas on whiteboards, sticky-notes with ideas on walls, are often created and interacted with during meetings. These shared artifacts a) facilitate the expression of complex fleeting ideas, b) enable collaborators to establish a common ground and validate each othersâ understanding about the context, and c) extend the validity of shared information by making it permanent. By the end of a collaboration session, the shared content denotes the shared knowledge amongst collaborators, which emerged as a result of a recursive process of storage, transformation, and retrieval from an external memory such as a whiteboard. Although these interactions with the artifacts symbolize the important episodes in a group discussion, still the information contained within them has not been much leveraged in collaboration research. Being well assimilated in the established work culture, collaborators do not heed the interactions with the shared artifacts, and therefore the nature of the social information contained within them is latent. However, from a research perspective this information is valuable and can offer insights into a few facets of ongoing group dynamics and processes. This thesis in particular a) identifies and examines the characteristics of the latent social information, b) studies the relationship of this information with different aspects of collaboration, and c) explores the practical utility of this information in collaboration assessment. We start by designing a meeting technology - MeetHub that enables collaborators to share and interact with artifacts in an unconstrained manner over a shared workspace, and allow us to collect fine-grained interactional information. Then we present user studies, where we extract and comprehend the relevant social information from interactions with the artifacts, and analyze its relationship with collaborative processes. Our findings demonstrate that latent social information is significantly correlated with the task outcome, division-of-labor, and the quality of mutual understanding between collaborators. Finally, we present a prediction system based on this social information, capable of alerting the group members about the poorly grounded episodes in real-time, and thus enabling them to regulate their collaborative behavior. The final contribution of this work presents itself as implications towards the dual nature of shared workspace, supporting the creation and sharing of artifacts as well as an assessment tool.